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COG250Y1 Lecture Notes - Prosopagnosia, Nelson Goodman, Hypnosis

Cognitive Science
Course Code
John Vervaeke

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Cognitive Science Notes
Generic Nominalism (weakest approach):
-All separate sciences contributing
-Cognition: set of processes by which human beings take in, store and transform & then
apply information in behaviour
-Problematic: because it suggests each contributing subject doesn‟t communicate with the
others cognition must be studied at various levels
Interdisciplinary Ecclecticism (stronger approach):
-Disciplines communicate, but no significant integration of them occurs
ex. Interfaith Dialogue
-Conferences 1 or 2 times a year
-Problematic: unstable resulting in: a) loss of interest and return to GN or
b) too weak to manage and becomes SI
Synoptic Integration (strongest approach):
-Integration of various levels of discourse and theory with others
-“From Philosophy to the Brain”
1. Plausible Theoretical Constructs
-Convergents increase in trustworthiness of construct (ex. Numbers)
2. Usefulness (Integration, Kitcher)
Ex. Hands= multi-apt (effectively function in many contexts)
Ex. Metaphor- consider processing, not product
-Aptness: correct description for transfer
a) Need a difference to make one (ex. bees are hornets)
b) Reasonable amount of correlation (ex. arguments are chairs)
-Multi-Apt: ex. F=ma unification of formulae elegance
-There should be a high level of profundity: depth of balanced convergence and
-Avoid biased „weirdness‟ (lack of trustworthiness)
-Science explains the familiar with the unfamiliar, but this is different than theoretical
-Get various disciplines to converge to explain many different levels
-Cognitive Science is governed by an imperative
-Naturalistic Explanations
Three Components of Naturalistic Imperative of Cognitive Science (Revolutions)
1. Thales
“All is moist. The lodestone is psyche”
Everything is made of water. The lodestone is the life force of living things.

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-Explaining the familiar with the unfamiliar
-Ontological Analysis: study of content of peoples‟ analyses separated from their
-Cognitive Science analyses goes from complex to basic
2. Scientific Revolution
-Rene Descartes formalized (explanation in terms of mathematical terminology)
*Avoid homuncular fallacy (presupposition of phenomena
you‟re trying to explain)
-We must attempt to explain in terms not of the mind
3. Turing
-Mechanization of formalisms empirical testing (computer as metaphor teaches us
about method)
-To make mind artificial intelligence
-Naturalistic Imperative in Cognition
Chapter 1: Categorization
-Category: usually refers to a group of objects in the world whereas
Concept: refers to a mental representation of such a group
Functions of Categorization
-Primary means for coding experience (Working Memory cannot process too much
information Categorization reduces the demands of cognitive processes)
-Concepts may vary in the extent to which they‟re used as codes (Ex. Taxonomy)
Inductive Inferences
-Inductive: inference considered improbable that new belief is false if old one is true
-Categorization infers invisible properties from visible ones, mentally
-Membership is often used to justify inference of invisible properties when in same group
Ex. Flamingo vs. Bat Aortas
-Most support for inductive inferences comes from basic & subordinate categories
-People are more likely to make I.Is about natural-kind categories, with regards to
invisible properties, than artefacts
-Inductive potential differs between classes of categories
Category Members
-Categorization is accomplished by: those aspects that are relatively common among
members of a category are incorporated into the concept representing that category
People decide whether something belongs to a category based in whether or not
its representation is sufficiently similar to the concept of interest
Geometric Approach

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1. Minimality: same distance for all items
2. Symmetry: distance between items is same no matter what
3. Triangular inequality: shortest distance between two points is a straight line
-Geometrical approach is successful at representing perceptual objects, but less so with
conceptual items
Problems with GA:
-Minimality: the more we know about an item, the more similar it is judged to itself
-Symmetry: an unfamiliar category is judged more similar to a familiar prominent
category, but not the other way around
-Triangular: implies A&C cannot be very dissimilar (Ex. Countries)
-“Nearest neighbour problem”: use of low dimentionality to represent categories
Featural Approach
-Item represented as a set of discrete features and the similarity between two items is
assumed to be an increasing function of the features they have in common and a
decreasing function of the features on which they differ
-Tversky‟s model:
-“Product-rule” model
-Contrast model does not tell us features of objects (must be determined)
-Contrast model does not offer theory of function „f‟ that measures the salience of each
set of features
-CM says little about algorithms used to implement computation
Analysis of Everyday Cognition
-Analyze, formalize, mechanize
-Categorization: a class of objects that we sense belong together
-Categorization affords the coding of experience
-Optimizes ability to deal with the world
-Bad for noticing individuals (stereotyping)
-Allows abstracting useful informationinductive inferences
-Inductive generalizing allows ability to learn about things in an indirect manner
-Improves communication (with self and other people)
Allows use of common nouns (Ex. yelling the word “fire!”
-Categorization seems like a basic process grouping by similarity and dissimilarity
-The obvious common sense needs explanation
“One of these things is not like the other, but all of them are kinda the same”- Sesame
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